Intelligent prediction of bundles of spare parts

ABSTRACT

Techniques for intelligently predicting bundles of replacement parts. A plurality of maintenance events for a plurality of replacement parts are determined based on historical data related to the plurality of replacement parts. Each maintenance event of the plurality of maintenance events corresponds with one or more of the replacement parts. A plurality of clusters of replacement parts are generated based on the plurality of maintenance events. A plurality of bundles of replacement parts are predicted based on the clusters. Each bundle includes a plurality of replacement parts.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of co-pending U.S. application Ser.No. 16/145,731, filed Sep. 28, 2018, which is hereby incorporated byreference in its entirety.

FIELD

Aspects of the present disclosure provide techniques for intelligentprediction of bundles of parts.

BACKGROUND

Companies that maintain a fleet of vehicles often need spare parts fortheir vehicles. For example, airlines need spare parts to maintain theirairplanes and to meet regulatory and safety requirements. These spareparts can be provided by Original Equipment Manufacturers (OEMs) orthird party resellers. An airline can purchase the necessary spare partsusing a search tool, for example an Internet based search tool providedby the part seller. The airline will often purchase only the parts itneeds for a specific maintenance task, to avoid unnecessary cost andstorage of unused parts.

To accomplish this, many airlines employ purchase agents, who maintain apredefined set of parts, for purchase, to use in the desired maintenancetask. Many factors can influence which parts the airline will purchasefor a maintenance task, including: the aircraft type, the type ofmaintenance, the condition of the aircraft, the airline's existinginventory, contracts with part suppliers, etc. Often, these parts areavailable and sold to the airlines individually, even when the airlineneeds a bundle of several parts to complete the desired maintenancetasks. In some cases, OEMs may be disadvantaged in these individual partsales, based on price or other factors.

SUMMARY

Embodiments described herein include a computer-implemented method forintelligently predicting bundles of replacement parts. The methodincludes determining, using a computer processor, a plurality ofmaintenance events for a plurality of replacement parts, based onhistorical data related to the plurality of replacement parts. Eachmaintenance event of the plurality of maintenance events correspondswith one or more of the replacement parts. The determining includesidentifying, using the computer processor, one or more replacement partsfor a maintenance event, based on one or more replacement part eventsoccurring within a time period related to the maintenance event. Themethod further includes generating, using the computer processor, aplurality of clusters of replacement parts based on the plurality ofmaintenance events. The method further includes predicting, using thecomputer processor, a plurality of bundles of replacement parts, basedon the clusters. Each bundle includes a plurality of replacement parts.

Embodiments described herein further include a system. The systemincludes a processor and a memory storing a program, which, whenexecuted on the processor, performs an operation. The operation includesdetermining a plurality of maintenance events for a plurality ofreplacement parts, based on historical data related to the plurality ofreplacement parts. Each maintenance event of the plurality ofmaintenance events corresponds with one or more of the replacementparts. The determining includes identifying one or more replacementparts for a maintenance event, based on one or more replacement partevents occurring within a time period related to the maintenance event.The operation further includes generating a plurality of clusters ofreplacement parts based on the plurality of maintenance events. Theoperation further includes predicting a plurality of bundles ofreplacement parts, based on the clusters. Each bundle includes aplurality of replacement parts.

Embodiments described herein further include a computer program productfor intelligently predicting bundles of replacement parts. The computerprogram product includes a computer-readable storage medium havingcomputer-readable program code embodied therewith, the computer-readableprogram code executable by one or more computer processors to perform anoperation. The operation includes determining a plurality of maintenanceevents for a plurality of replacement parts, based on historical datarelated to the plurality of replacement parts. The determining includesidentifying one or more replacement parts for a maintenance event, basedon one or more replacement part events occurring within a time periodrelated to the maintenance event. Each maintenance event of theplurality of maintenance events corresponds with one or more of thereplacement parts. The operation further includes generating a pluralityof clusters of replacement parts based on the plurality of maintenanceevents. The operation further includes predicting a plurality of bundlesof replacement parts, based on the clusters. Each bundle includes aplurality of replacement parts.

BRIEF DESCRIPTION OF THE DRAWINGS

So that the manner in which the above recited features of the presentdisclosure can be understood in detail, a more particular description ofthe disclosure, briefly summarized above, may be had by reference toaspects, some of which are illustrated in the appended drawings.

FIG. 1 is a block diagram illustrating intelligent prediction of sparepart bundles, according to one embodiment described herein.

FIG. 2 is a block diagram illustrating a bundle prediction server,according to one embodiment described herein.

FIG. 3 is a flow chart illustrating intelligent prediction of spare partbundles, according to one embodiment described herein.

FIG. 4 is a flow chart illustrating identifying relevant historical partsales data, according to one embodiment described herein.

FIG. 5 is a flow chart illustrating identifying relevant historicalsearch data, according to one embodiment described herein.

FIG. 6 is a flow chart illustrating accounting for interchangeable spareparts, according to one embodiment described herein.

FIG. 7 is a flow chart illustrating determining maintenance events,according to one embodiment described herein.

FIG. 8 is a flow chart illustrating generating a matrix of partrelationships, according to one embodiment described herein.

FIG. 9 is a flow chart illustrating intelligent prediction of partbundles, according to one embodiment described herein.

FIG. 10 is an illustration of an example matrix of part relationships,according to one embodiment described herein.

DETAILED DESCRIPTION

In addition to (or instead of) offering spare parts for saleindividually, OEMs and other part sellers can sell bundles of partstargeted to a particular maintenance task. This is more convenient forthe customer, because all the necessary parts for a task are providedtogether, and allows the part seller to more competitively price theparts, as a bundle. But the seller typically does not know which parts acustomer will need, before the customer seeks the parts, and thereforedoes not know which parts should be bundled and offered for saletogether.

Aspects of the present disclosure relate to techniques for intelligentprediction of spare part bundles by a computer. Relevant historical dataabout part sales and electronic searches can be identified usingspecific computerized techniques, and used to intelligently predictwhich parts an airline is likely to purchase together. This allows forreduced pricing of the part bundles, allowing a part seller to convert ahigher number of sales. Further, the predicted bundles are moreconvenient for customers, and can assist a customer in recognizing partsthat it should consider purchasing for a given task. In addition,certain techniques disclosed herein provide for efficient prediction ofspare part bundles, limiting the computational resources needed. Thesetechniques improve the operation of the computer by efficiently andeffectively providing for intelligent prediction of spare part bundles.

The predicted bundles can be offered to customers in numerous ways. Inone embodiment, the bundles can be listed for sale, as bundles, and acustomer can select a desired bundle. In another embodiment, when acustomer electronically searches for (or selects) a given part, bundleswhich contain that part are found, and additional parts from thosebundles are offered to the customer. In an embodiment, the predictedbundles are maintained for a relatively long period of time (e.g.,weeks, months, or even years), and do not change frequently. Further, inan embodiment, the predicted bundles are predictions of specificcustomer requirements for maintenance events.

The techniques discussed below can be particular effective for highvalue vehicles like airplanes, helicopters, etc. For example, asdiscussed below in relation to FIGS. 7-10, in an embodiment techniquesdescribed herein can be used to identify maintenance events during whicha customer purchases several parts over a particular time period. Theidentified maintenance events can be used to predict bundles of parts.This is particularly applicable to airplanes and other high valuevehicles because parts purchases for these vehicles are often predicableand well thought out by customers. For example, because commercialairplane parts are often expensive, many airlines practice relativelylean business strategies. The airlines only purchase the parts theyneed, and purchase the parts only on a regimented schedule or because ofa documented need. The airlines generally do not make spur of the momentpurchases. An effectively predicted bundle should take this intoaccount.

The techniques described herein are particularly suitable for thisenvironment, because they can identify parts purchased together in bothcircumstances: as part of a scheduled maintenance event (which can varybetween customers) and as part of a recurring but unscheduled need. Forexample, the techniques described in relation to FIGS. 7-10 can identifymaintenance events and predict part bundles related to scheduledmaintenance, without requiring knowledge of each customer's maintenanceschedule. That is, these techniques can predict bundles of parts withoutrequiring access to a customer's maintenance or purchase schedule.Further, these techniques can identify parts that are purchased as partof an unscheduled maintenance event—for example, parts that often mustbe replaced at similar times, even when not scheduled for replacementtogether (e.g., because multiple vehicle may need replacement of thesame parts at similar times, or because a need for one part may be asignal that another part will likely be needed). A bundle can bepredicted even when a customer itself does not realize it will need topurchase the bundled parts together, and when no observer has recognizedthat the parts should be bundled.

Further, one or more of the techniques described herein are particularlyapplicable to well regulated industries, like commercial air travel.Governmental regulations will sometimes require changes to a part tocomply with new or modified regulations. The modified part may beinterchangeable with a previously available part, meaning, for example,that the new part can and should be used in place of the previouslyavailable part. As discussed in relation to FIG. 6, below, thisinterchangeability relationship can be used to accurately include thereplacement part in a predicted bundles, even where the replacement partmay not yet be included in historical sales.

The techniques discussed herein are not limited to high value vehicleslike airplanes, helicopters, etc. They can be suitable to any number ofindustries and contexts. For example, some (or all) of the techniquesdescribed may be applicable to other transportation and freightindustries that incorporate vehicles, including bus lines, taxicompanies, shipping lines, cruise lines, rail lines, transitauthorities, and many others. Further, some (or all) of the techniquesdescribed may be applicable to other industries, including factories,manufacturing facilities, and other suitable industries or contexts.

FIG. 1 is a block diagram illustrating a system 100 for intelligentprediction of spare part bundles, according to one embodiment describedherein. A bundle prediction server 200 is connected to a communicationnetwork 110. The bundle prediction server 200 is described in moredetail with regard to FIG. 2. The bundle prediction server 200 isgenerally configured to implement intelligent prediction of spare partbundles, along with pricing of the bundles.

The communication network 110 can be any suitable communication network,including the Internet, a local access network, or a wide accessnetwork. The communication network 110 can be a wired or wirelessnetwork. The communication network can use any suitable communicationprotocol, including any suitable wireless protocol. For example, thecommunication network 110 can use an Institute for Electrical andElectronics Engineers (IEEE) Wi-Fi standard, like an 802.11 standard,another Wi-Fi standard, a cellular protocol (including 3G, Long-TermEvolution (LTE), 4G and others), Bluetooth, and others. Further, thecommunication network 110 can use several different communicationprotocols.

The communication network 110 is further connected to the datarepository 170. The data repository 170 can be any suitable data storagemedium. For example, the data repository 170 can include a relationaldatabase, or any other suitable database. In an embodiment, the datarepository 170 includes network interface software and hardware to allowfor communication with the communication network 110. For example, thedata repository 170 can include a server computer with a networkinterface. As another example, the data repository 170 could be includedwithin the bundle prediction server 200. Alternatively, as discussedfurther below, the data repository 170 could be a cloud-based storagesystem, accessible via the communication network 110.

The data repository 170 includes data for use by the bundle predictionserver 200 in intelligent prediction of spare part bundles, along withpricing the bundle. In the illustrated embodiment, the data repository170 includes part interchangeability data 172. The partinterchangeability 172 include data describing parts that can beconsidered interchangeable for each other. For example, an older sparepart may be replaced by a new version with a different part number. Thiscould be done in response to a change in governmental regulations,because of an improvement in manufacturing or materials, or for anynumber of reasons. The newer part may be considered interchangeable forthe older part, so that a customer seeking to purchase the older partcan be directed to the newer part. But the reverse may not be true—thenewer part may have been refined, or improved, so that the older part isnot considered interchangeable for the newer part. The partinterchangeability 172 can include data describing this relationship.

The data repository 170 further historical sales 174. The historicalsales 174 include, for example, data describing which parts have beensold, to which customers, at which times. The historical sales 174 caninclude any suitable information describing historical sales of parts,including part identifiers for the parts sold, customer information,pricing information, date information, and other relevant information.The data repository 170 further includes historical searches 176. Thehistorical searches 176 include, for example, data about which parts acustomer has electronically searched for (e.g., based on a partidentifier), at what time, whether or not the customer chose to purchasethe parts. In some situations, a customer will electronically search forparts but choose not to purchase them. For example, a customer might usean OEM search tool to identify which parts it would like to purchase,but might purchase the parts from a third party seller. The historicalsearches 176 capture customers' electronic searches for parts, whetheror not the customer chooses to purchase the parts. In an embodiment, thehistorical searches 176 can include information describing whether agiven electronic search ended up resulting in a sale. The illustrateddata in the data repository 170 are merely examples, and other data canalso be included.

FIG. 2 is a block diagram illustrating a bundle prediction server 200,according to one embodiment described herein. As shown, the bundleprediction server 200 includes, without limitation, a central processingunit (CPU) 202, a network interface 206, a memory 210, and storage 270,each connected to a bus 208. In an embodiment, the bundle predictionserver 200 also includes an Input/Output (I/O) device interface 204 forconnecting to I/O devices 260. In an embodiment, the I/O devices 260 canbe external I/O devices (e.g., keyboard, display and mouse devices).Alternatively, the I/O devices 260 can be built in I/O devices (e.g., atouch screen display or touchpad). Further, in context of thisdisclosure, the computing elements shown in the bundle prediction server200 may correspond to a physical computing system (e.g., a system in adata center) or may be a virtual computing instance executing within acomputing cloud, as discussed further below.

The CPU 202 retrieves and executes programming instructions stored inthe memory 210 as well as stores and retrieves application data residingin the storage 270. The bus 208 is used to transmit programminginstructions and application data between the CPU 202, the I/O deviceinterface 204, the storage 270, the network interface 206, and thememory 210. The CPU 202 is included to be representative of a CPU,multiple CPUs, a single CPU having multiple processing cores, graphicsprocessing units (GPUs) having multiple execution paths, and the like.The memory 210 is generally included to be representative of electronicstorage of any suitable type(s), including random access memory ornon-volatile storage. The storage 270 may be a disk drive storagedevice. Although shown as a single unit, the storage 270 may be acombination of fixed and/or removable storage devices, such as fixeddisc drives, removable memory cards, network attached storage (NAS), ora storage area-network (SAN).

Illustratively, the memory 210 includes an operating system 240 and adatabase management system (DBMS) 250, while the storage 270 includes adata repository 170 (e.g., a database). The operating system 240generally controls the execution of application programs on the bundleprediction server 200. Examples of operating system 240 include, withoutlimitation, versions of UNIX, distributions of the Linux® operatingsystem, versions of Microsoft® Windows® and so on. The DBMS 250generally facilitates the capture and analysis of data in the datarepository 170 (e.g., spare part data). For instance, the DBMS 250 couldenable the definition, creation, querying, update and administration ofthe data repository 170. As an example, the DBMS 250 could receive aquery (e.g., composed using Structured Query Language (SQL)) and, inresponse, could generate an execution plan that includes one or moreaccess routines to be run against the data repository 170. The DBMS 250could then execute the access routine(s) and could return any queryresult data to the requestor.

The memory 210 generally includes program code for performing variousfunctions related to intelligent prediction of spare part bundles, alongwith pricing the bundles. The program code is generally described asvarious functional “applications,” “components,” or “modules” within thememory 210, although alternate implementations may have differentfunctions and/or combinations of functions. Within the memory 210, thebundle prediction module 220 is generally configured to intelligentlypredict bundles of spare parts. This is described further with regard toFIGS. 3-9.

The memory 210 further includes a bundle pricing module 230. The bundlepricing module 230 is generally configured to determine prices for thebundles predicted by the bundle prediction module 220. In an embodiment,this can be done automatically based on historical data (e.g., data inthe data repository 170). Alternatively, this can be done basedpartially, or completely, on input from a subject matter expert or otheruser.

FIG. 3 is a flow chart 300 illustrating intelligent prediction of sparepart bundles according to one embodiment described herein. At block 302,a bundle prediction module (e.g., the bundle prediction module 220illustrated in FIG. 2) identifies relevant historical sales data (e.g.,the historical sales data 174 illustrated in FIG. 1) for use inintelligent prediction of spare part bundles. In an embodiment, thebundle prediction module 220 selects a subset of relevant historicalsales data 174 for use in the intelligent prediction. This isillustrated further in relation to FIG. 4.

At block 304, a bundle prediction module (e.g., the bundle predictionmodule 220 illustrated in FIG. 2) identifies relevant historical searchdata (e.g., the historical search data 176 illustrated in FIG. 1) foruse in intelligent prediction of spare part bundles. In an embodiment,the bundle prediction module 220 selects a subset of relevant historicalsearch data 176 for use in the intelligent prediction. This isillustrated further in relation to FIG. 5.

At block 306, a bundle prediction module (e.g., the bundle predictionmodule 220 illustrated in FIG. 2) accounts for interchangeable parts foruse in prediction of spare part bundles. In an embodiment, the bundleprediction module 220 uses data about the interchangeability of spareparts (e.g., the part interchangeability 172 illustrated in FIG. 1) todetermine which parts are interchangeable for purposes of predictingspare part bundles. This is illustrated further in relation to FIG. 6.

At block 308, a bundle prediction module (e.g., the bundle predictionmodule 220 illustrated in FIG. 2) determines maintenance events for usein predicting spare part bundles. In an embodiment, an airline (or otherpurchaser) may purchase, or electronically search for, parts related toa specific maintenance event in a series of transactions. That is,instead of purchasing (or searching for) the spare parts used for aparticular event all at once, the airline may purchase the parts over aseries of hours, days, weeks, or even months. In an embodiment, theparts used for this event should be bundled together, but the timeperiod during which the airline was purchasing (or searching for)related parts is not known. In an embodiment, the bundle predictionmodule 220 uses the historical sales 174 and historical searches 176 toidentify the time periods during which a customer's activities wererelated a particular maintenance event. This is described in more detailin relation to FIG. 7.

At block 310, a bundle prediction module (e.g., the bundle predictionmodule 220 illustrated in FIG. 2) generates a matrix of relationshipsbetween spare parts, for use in predicting bundles of spare parts. In anembodiment, the maintenance events determined at block 308 can be usedto generate a matrix (e.g., a binary matrix) describing relationshipsbetween spare parts. This is described in more detail in relation toFIG. 8. At block 312, a bundle prediction module (e.g., the bundleprediction module 220 illustrated in FIG. 2) predicts bundles of spareparts using the matrix generated at block 310. This is described in moredetail in relation to FIG. 9.

At block 314, a bundle pricing module (e.g., the bundle pricing module230 illustrated in FIG. 2) generates a price for the part bundlespredicted at block 312. In an embodiment, the bundle pricing module 230can automatically generate the price of the bundle based on, forexample, pricing information for the individual components, historicalsales data, and characteristics of the parts (e.g., the age of thedestination vehicle, the popularity of the part, etc.). In anembodiment, bundle prices are relatively stable, and unlikely to changefrequently. In another embodiment, bundle prices can change dynamicallybased on numerous factors, including customer demand, the prices andcosts of the individual parts in the bundle, sales targets, etc.

In one example, the bundle pricing module 230 can consider the price ofeach individual part in a bundle, and apply a discount to the combinedprice. This can be a set discount (e.g., a percentage of the totalprice) or can be determined based on the underlying cost and desiredprofit or profit margin from sale of the bundle. In an embodiment,offering the bundle for sale together allows for a discounted rate,because of the larger number of individual parts and relatively largersales price of the bundle. As another example, the bundle pricing module230 can consider historical sales data (e.g., historical sales 174illustrated in FIG. 1) when generating the bundle price. As anotherexample, the bundle pricing module 230 can consider characteristics ofthe parts in the bundle when generating the bundle price. For example,if the bundle relates to a relatively older, but common, model ofvehicle (e.g., an older model of airplane), the bundle price could belowered accordingly. Alternatively, if the bundle relates to arelatively rare vehicle, the price could be kept relatively higher.Similarly, if the bundle relates to relatively popular or off-the-shelfparts, the bundle price could be lowered. If the bundle relates tospecial order or more rare parts, the bundle price could be raised.

In another embodiment, the bundle pricing module 230 can rely on userinput to generate a price for the bundles. For example, a subject matterexpert (or other user) could be prompted to enter a price for thebundle. Alternatively, the bundle pricing module 230 can combine userinput with automatic pricing. For example, a user could provide upperand lower boundaries for the price, a desired discount rate, a targetprofit amount or profit margin, etc. The bundle pricing module 230 canuse this user input to generate pricing for the bundles.

FIG. 4 is a flow chart illustrating identifying relevant historical partsales data according to one embodiment described herein. In anembodiment, FIG. 4 corresponds with block 302 illustrated in FIG. 3. Atblock 402, the bundle prediction module (e.g., the bundle predictionmodule 220 illustrated in FIG. 2) receives the historical sales data 400(e.g., the historical sales 174 illustrated in FIG. 4) and identifiespotentially relevant sales. For example, sales can be screened by dateor by destination. As one example, sales occurring prior to a particularcut-off date can be removed. Similarly, bundle prediction can betargeted to a particular end product (or end product family), like aparticular airplane model, and sales not related to the target can beremoved.

At block 404, the bundle prediction module 220 identifies potentiallyrelevant sales by type. In an embodiment, this can include type of sale,type of customer, or both. For example, historical sales can bedesignated with a code denoting the type of sale—the code can be used todistinguish between one time sales, repeated sales, etc. Similarly,historical sales can be designated with a code denoting the type ofcustomer purchasing the item, for example airlines, entities focusing onmaintenance repair and overhaul (MRO), distributors, brokers, etc. Inone example, a sale might be the result of a governmental requirement orrecall. This sale would not expect to be repeated, so it can be excludedfrom the bundle prediction analysis. In an embodiment, sales likely tobe repeated are included in the bundle prediction analysis while saleslikely to be one-time-only are excluded.

At block 406, the bundle prediction module 220 identifies relevant salesdata by the status of the sales. In an embodiment, a given sale has anassociated code denoting the phase of the sales process. For example,from the time a customer orders a part, until the time the part ships,there can be many different phases—including, ordering, manufacturing,shipping, etc. In an embodiment, the associated code can be used toinclude sales that were consummated, and exclude sales that wererejected or cancelled. After block 406, the bundle prediction module 220outputs the selected historical sales 410.

FIG. 5 is a flow chart illustrating identifying relevant historicalsearch data, according to one embodiment described herein. In anembodiment, FIG. 5 corresponds with block 304 illustrated in FIG. 3. Atblock 502, the bundle prediction module (e.g., the bundle predictionmodule 220 illustrated in FIG. 2) receives the historical search data500 (e.g., the historical searches 176 illustrated in FIG. 4) andidentifies potentially relevant searches. For example, searches can bescreened by date. As one example, searches occurring prior to aparticular cut-off date can be removed.

At block 504, the bundle prediction module 220 identifies relevantsearches by user type, customer type, or both. In an embodiment, eachsearch can be associated with an internal (e.g., within the part seller)or external (e.g., customer or potential customer) user. At block 504,the bundle prediction module 220 can remove searches by internal users.In an embodiment, the bundle prediction module 220 distinguishes betweenusers searching with an intent to purchase parts and users searching forresearch or other purposes. In an embodiment, this can be done based ondistinguishing between customer types, for example airlines, MRO,distributors, brokers, etc., as well as (or instead of) internal vsexternal users. In an embodiment, searches relating to users with anintent other than purchasing parts are excluded. Alternatively, bothhistorical sales data (illustrated in relation to FIG. 4) and historicalsearch data can be identified based on user or customer type, excludingusers or customers unlikely to make further purchases.

At block 506, the bundle prediction module 220 identifies relevantsearch data by end product. In an embodiment, bundle prediction can betargeted to a particular end product (or end product family), like aparticular airplane model, and searches not related to the target can beremoved. At block 508, the bundle prediction module 220 identifiesrelevant search data by availability. In an embodiment, the bundleprediction module 220 can remove search data relating to parts that arenot active and available for purchase in the part sellers system. Afterblock 508, the bundle prediction module 220 outputs the selectedhistorical searches 510. In an embodiment, the bundle prediction module220 can combine the selected historical sales 410 and the selectedhistorical searches 510.

FIG. 6 is a flow chart illustrating accounting for interchangeable spareparts, according to one embodiment described herein. In an embodiment,FIG. 6 corresponds with block 306 illustrated in FIG. 3. At block 602,the bundle prediction module (e.g., the bundle prediction module 220illustrated in FIG. 2) receives part interchangeability data (e.g., partinterchangeability 172 illustrated in FIG. 1). At block 604, the bundleprediction module 220 selects the next active part. In an embodiment,the bundle prediction module 220 can select from a list of all activeparts in the part sellers database. Alternatively, the bundle predictionmodule 220 can select from active parts present in historical sales orsearch data, or another data source.

At block 606, the bundle prediction module 220 determines whether areplacement is available for the selected part. In an embodiment, thiscan be denoted by a field associated with the part in a centraldatabase. In an embodiment, the associated field can designate whether apart is one way interchangeable, two way interchangeable, or notinterchangeable. A one way interchangeable part can be replaced by adesignated part (or parts). A two way interchangeable part can both bereplaced by a designated part (or parts), and can serve as a replacementfor a designated part (or parts). If the selected part is notinterchangeable, at block 608 the bundle prediction module 220 uses theactive part identifier and proceeds to block 612.

If the selected part is interchangeable, at block 610 the bundleprediction module 220 identifies the most recent replacement part. In anembodiment, this most recent replacement part can be used to substitutefor the active part in a predicted part bundle. In an embodiment, thebundle prediction module 220 identifies the most recent replacement partby examining data relating to a chain of part. For example, the selectedpart can include data designating a newer replacement (e.g., by partidentifier). But this may not be the newest replacement. The bundleprediction module 220 can identify the newer replacement (i.e., the nextlink in the chain, by part identifier) and look for a designatedreplacement for that part. If the newer replacement includes its ownreplacement, the process proceeds, until the bundle prediction module220 identifies the newest replacement part (e.g., by part identifier).At block 612, the bundle prediction module 220 determines whether alldesired active parts have been examined. If so, the flow ends. If not,the bundle prediction module 220 returns to block 604 and selects thenext active part.

FIG. 7 is a flow chart illustrating determining maintenance events,according to one embodiment described herein. In an embodiment, FIG. 7corresponds with block 308 illustrated in FIG. 3. As discussed above,many maintenance events involve purchases, or searches, for parts spreadout over multiple transactions. These transactions can be spread outover hours, days, weeks, or even months. FIG. 7 describes one embodimentfor identifying which transactions (i.e., sales or searches) relate tothe same maintenance event.

At block 702, the bundle prediction module (e.g., the bundle predictionmodule 220 illustrated in FIG. 2) combines historical sales andhistorical search data. In an embodiment, the bundle prediction module220 combines the selected historical sales data 410, illustrated in FIG.4, with the selected historical search data 510, illustrated in FIG. 5.

At block 704, the bundle prediction module 220 accounts forinterchangeability of parts in the combined historical sales and searchdata. As discussed above in relation to FIG. 6, in an embodiment some ofthe parts can have interchangeable counterparts. For example, an olderpart may have been replaced with a new part (e.g., due to changes ingovernmental regulations or improved manufacturing techniques), and thenew part may be interchangeable with the old part. At block 704, bundleprediction module 220 accounts for this by combining the combinedhistorical sales and search data with the interchangeability data. Anyparts listed in the combined historical sales and search data that havenewer, interchangeable, counterparts, are replaced with the latestcounterpart. For example, a historical sale involving an older part withtwo interchangeable replacements, would have the part number for theoriginal, older, part replaced with the newest interchangeable part.This allows the predicted part bundles to include the newest parts,where possible.

At block 706, the bundle prediction module 220 sorts the data resultingfrom block 704 by customer and date. In an embodiment, the bundleprediction module 220 sorts the data by customer, and then by datewithin each customer. Alternatively, the bundle prediction module cansort the data by date, and then by customer. At block 708, the bundleprediction module 220 creates time series data, for each customer, basedon the output from block 706. In an embodiment, the bundle predictionmodule 220 converts the date at which a sale was placed, or a search wasconducted, to a number. The bundle prediction module 220 then createstime series data for each customer, using the numbers.

At block 710, the bundle prediction module 220 calculates a kerneldensity estimate for each customer. Kernel density estimation is astatistical technique that can be used to estimate the underlyingprobability density function of a dataset. Kernel density estimation isan example of one technique that can be used. Other suitable techniquescan also be used. In an embodiment, the bundle prediction module 220 cancalculate the kernel density estimation for the time series datarelating to each customer, using a bandwidth of 1. Alternatively, otherbandwidths could be used.

At block 712, the bundle prediction module 220 identifies themaintenance event time periods for the various customers. In anembodiment, the bundle prediction module 220 can identify the minimumpoints along the kernel density estimation calculated at block 710. Thebundle prediction module 220 can then identify two consecutive minimumpoints along the density estimation. These are the likely boundaries ofthe time period (e.g., the starting point in time and the ending pointin time), for a given maintenance event.

At block 714 the bundle prediction module 220 identifies parts sold orsearched for within the time periods identified at block 712. These arethe parts related to the event occurring during that time period. Forexample, at block 712 the bundle prediction module 220 can determineDate A as the likely beginning of a given time period and Date B as thelikely end. At block 714, the bundle prediction module identifies partssold, or search for, between Date A and Date B, using the combinedhistorical sales and search data.

FIG. 8 is a flow chart illustrating generating a matrix of partrelationships, according to one embodiment described herein. In anembodiment, FIG. 8 corresponds with block 310 illustrated in FIG. 3. Atblock 802, the bundle prediction module (e.g., the bundle predictionmodule 220 illustrated in FIG. 2) receives the maintenance eventsidentified in FIG. 7. At block 804, the bundle prediction module 220constructs a string from each event. In an embodiment, the stringincludes the part numbers corresponding to the event (e.g., asidentified in FIG. 7), with the part numbers separated with a delimiter,like a space, tab, or comma.

At block 806, the bundle prediction module 220 constructs a matrixcorresponding to the event strings constructed at block 804. In anembodiment, this can be a two dimensional binary matrix (e.g., a twodimensional binary array), with one dimension representing theidentified events and the other dimension representing the partsincluded in each identified event. One example is illustrated in FIG.10, discussed further below. Alternatively, this can be a matrix with adifferent number of dimensions, and the values in the matrix can benon-binary (e.g., integers, real numbers, booleans, a defined type,etc.).

At block 808, the bundle prediction module 220 calculates the localweight of the matrix entries. The local weight is used to identify thecontribution of each part to the maintenance event including that part.In an embodiment, a matrix entry corresponds to a particular part and aparticular event. The local weight of that matrix entry represents thecontribution of that particular part to that particular event (e.g., theimportance of that part to that event).

At block 810, the bundle prediction module 220 calculates the globalweight of each part included in the matrix. The global weight is used toidentify the contribution of a given part to all of the identifiedevents (e.g., the importance of that part to all the events). In anembodiment, the global weight can be calculated using the formula:

${{globalWeight}({part\_ x})} = {{\log \left( {\frac{\# \mspace{14mu} {events}\mspace{14mu} {including}\mspace{14mu} {part\_ x}}{\# \mspace{14mu} {total}\mspace{14mu} {events}} + 1} \right)}.}$

At block 812, the bundle prediction module 220 reduces the matrixdimension. In an embodiment, the bundle prediction module 220 can reducethe complexity of predicting part bundles by removing from the matrixless important values. This allows the bundle prediction module 220 tofocus on the more important value, and reduces noise. In an embodiment,the bundle prediction module 220 multiplies the calculated weightmatrices from blocks 808 and 810 together to generate a resultantmatrix. The bundle prediction module 220 then calculates thesingular-value decomposition (SVD) of the resultant matrix. The bundleprediction module 220 reduces the dimension of the matrix from the SVDfor eigenvalues less than a pre-determined value. Using SVD, asdiscussed above, is one example of a technique for reducing the matrix.Alternatively, other techniques could be used instead.

FIG. 9 is a flow chart illustrating intelligent prediction of partbundles, according to one embodiment described herein. In an embodiment,FIG. 9 corresponds with block 312 illustrated in FIG. 3. At block 902,the bundle prediction module (e.g., the bundle prediction module 220illustrated in FIG. 2) receives the matrix (e.g., the matrix generatedusing the techniques illustrated in FIG. 8, above). At block 904, thebundle prediction module 220 calculates the Euclidian distance betweenparts in the matrix.

At block 906, the bundle prediction module 220 clusters the parts in thematrix, using the Euclidian distance calculated at block 904. In anembodiment, the bundle prediction module 220 performs hierarchicalclustering and uses Ward's minimum variance method for the clustering.Alternatively, another method of hierarchical clustering can be used, oranother form of clustering can be used. At block 908, the bundleprediction module 220 predicts the bundles by collecting the clusteredpart numbers into bundles.

FIG. 10 is an illustration of an example matrix 1000 of partrelationships, according to one embodiment described herein. In anembodiment, the illustrated matrix in FIG. 10 is an example of a matrixgenerated according to techniques illustrated in FIG. 8. In anembodiment, the illustrated matrix can be implemented using a twodimensional binary array. The embodiment illustrated in FIG. 10 ismerely an example of a possible matrix—many other formats and types ofmatrices can be used. In the matrix 1000, the rows represent parts(e.g., identified by part number) and the columns represent maintenanceevents (e.g., identified according to techniques illustrated in FIG. 7).In an embodiment, each part included in at least one event is includedas a row in the matrix 1000. Alternatively, each active part availablefor sale by the part seller is included as a row in the matrix 1000.

Each entry in the matrix 1000 represents whether the event in thatcolumn includes the part in that row. A binary “1” signifies that theevent in that column includes the part in that row, while a binary “0”signifies that the event in that column does not include that part. Forexample, Event A includes parts 1, 3, 6, and 7, but does not includeparts 2, 4, 5, and 8. Event B includes parts 3, 4, 5, 6, and 7, but doesnot include parts 1 and 8. In an embodiment, as discussed above, thematrix 1000 can be used to cluster parts and predict bundles of parts.

After a bundle prediction module (e.g., the bundle prediction module220) predicts a bundle of parts (e.g., as described in relation to block312 in FIG. 3), the bundle can be sent to a customer. In an embodiment,the associated price can also be sent to the customer (e.g., a pricegenerated using the bundle pricing module 230). In an embodiment, thebundle prediction module can generate a user interface for display ofthe bundle information to the customer—for example, the bundleprediction module can generate a webpage (e.g., and HTML web page), aJavascript module, an XML module, or another suitable interfacedescription. The bundle prediction module (or another suitable module)can provide this user interface description to a customer for display bythe customer. For example, the bundle prediction module can transmit theuser interface description (e.g., the HTML, Javascript, or XML module)over a communication network to the customer for display by a customer'sweb browser or application. Further, as another example, the bundleprediction module (or another suitable module) can communicate with acustomer device over a communication network (e.g., using a suitableApplication Programming Interface (API)).

In an embodiment, the customer can select a desired bundle for purchase(e.g., using the user interface). The customer's computer, or otherelectronic device, can transmit a response signal to the bundleprediction server (e.g., the bundle prediction server 200) or anothersuitable server. After receiving the response signal, the server canfulfil the purchase. For example, the server can reserve the parts inthe bundle in inventory, to facilitate fulfilment. The server canfurther identify the destination for the parts in the bundle, and cantrigger processing and shipment of the parts. In an embodiment, theserver can fulfil the order automatically. Alternatively, the server canalert fulfilment personnel (e.g., using a user interface) and providethe necessary information to allow the fulfilment personnel to obtainand ship the purchased parts. The parts may then be shippedautomatically or by fulfillment personnel.

In an embodiment, purchase of the bundled parts can be done through auser interface provided to customers. Alternatively, purchase could bedone automatically. For example, a customer could provide pre-approvalfor a seller to predict parts that will likely be necessary at aparticular point in time, and to automatically generate a bundle andpurchase the parts. In an embodiment, this pre-approval could be furthertied to a desired or maximum price for the bundle. The customer could beprovided with the ability to approve or reject the purchase prior tocompletion, or the purchase could be fully automated. In an embodiment,a customer could further provide approval for automatic purchase of apredicted bundle when the customer seeks to purchase an individual part.For example, a customer could seek purchase of a particular part, andthe system could identify a predicted bundle associated with that partand automatically purchase the bundle. This could be done with customerapproval at the time of purchase, or automatically based onpre-approval. In an embodiment, the accuracy of the predicted bundlescan encourage a customer to pre-approve automatic bundle prediction forfuture purchases.

In the preceding, reference is made to embodiments presented in thisdisclosure. However, the scope of the present disclosure is not limitedto specific described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practicecontemplated embodiments. Furthermore, although embodiments disclosedherein may achieve advantages over other possible solutions or over theprior art, whether or not a particular advantage is achieved by a givenembodiment is not limiting of the scope of the present disclosure. Thus,the preceding aspects, features, embodiments and advantages are merelyillustrative and are not considered elements or limitations of theappended claims except where explicitly recited in a claim(s).

As will be appreciated by one skilled in the art, the embodimentsdisclosed herein may be embodied as a system, method or computer programproduct. Accordingly, aspects may take the form of an entirely hardwareembodiment, an entirely software embodiment (including firmware,resident software, micro-code, etc.) or an embodiment combining softwareand hardware aspects that may all generally be referred to herein as a“circuit,” “module” or “system.” Furthermore, aspects may take the formof a computer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, Radio Frequency (RF), etc., or anysuitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++, R, SAS or the like and conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute entirelyon the user's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present disclosure are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodimentspresented in this disclosure. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

One or more embodiments may be provided to end users through a cloudcomputing infrastructure. Cloud computing generally refers to theprovision of scalable computing resources as a service over a network.More formally, cloud computing may be defined as a computing capabilitythat provides an abstraction between the computing resource and itsunderlying technical architecture (e.g., servers, storage, networks),enabling convenient, on-demand network access to a shared pool ofconfigurable computing resources that can be rapidly provisioned andreleased with minimal management effort or service provider interaction.Thus, cloud computing allows a user to access virtual computingresources (e.g., storage, data, applications, and even completevirtualized computing systems) in “the cloud,” without regard for theunderlying physical systems (or locations of those systems) used toprovide the computing resources.

Typically, cloud computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space consumed by auser or a number of virtualized systems instantiated by the user). Auser can access any of the resources that reside in the cloud at anytime, and from anywhere across the Internet. In context of the presentdisclosure, a user may access applications (e.g., bundle predictionserver 200) or related data available in the cloud. For example, one orboth of the bundle prediction module 220 and the bundle pricing module230 could execute on a computing system in the cloud. In such a case,each module could access data stored at a storage location in the cloud(e.g., historical sales and search data), and could store associateddata in the cloud. Doing so allows a user to access this informationfrom any computing system attached to a network connected to the cloud(e.g., the Internet).

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality and operation of possible implementations ofsystems, methods and computer program products according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block may occurout of the order noted in the figures. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

In view of the foregoing, the scope of the present disclosure isdetermined by the claims that follow.

What is claimed is:
 1. A computer-implemented method for intelligently predicting bundles of replacement parts, the method comprising: determining, using a computer processor, a plurality of maintenance events for a plurality of replacement parts, based on historical data related to the plurality of replacement parts, wherein each maintenance event of the plurality of maintenance events corresponds with one or more of the replacement parts, the determining comprising: identifying, using the computer processor, one or more replacement parts for a maintenance event, based on one or more replacement part events occurring within a time period related to the maintenance event; generating, using the computer processor, a plurality of clusters of replacement parts based on the plurality of maintenance events; and predicting, using the computer processor, a plurality of bundles of replacement parts, based on the clusters, wherein each bundle comprises a plurality of replacement parts.
 2. The method of claim 1, wherein the one or more replacement part events comprise at least one of sales of replacement parts or electronic searches for replacement parts, and wherein the time period relating to the maintenance event comprises a time period relating to a starting point in time for the maintenance event and an ending point in time for the maintenance event.
 3. The method of claim 1, wherein determining, using the computer processor, the plurality of maintenance events for the plurality of replacement parts further comprises: generating time series data related to the historical data; and calculating a kernel density estimate related to the time series data, wherein the time period related to the maintenance event is based on the kernel density estimate.
 4. The method of claim 1, further comprising: identifying, using the computer processor, a plurality of interchangeable parts among the plurality of replacement parts, wherein the plurality of maintenance events is based on the plurality of interchangeable parts.
 5. The method of claim 4, further comprising: determining, using the computer processor, for a first interchangeable part of the plurality of interchangeable parts, an original part identifier and a most recent part identifier, wherein the plurality of maintenance events uses the most recent part identifier in place of the original part identifier.
 6. The method of claim 1, further comprising: generating, using the computer processor, a two dimensional array relating to the plurality of maintenance events and the plurality of replacement parts, wherein generating, using the computer processor, the plurality of clusters of replacement parts is based on the two dimensional array.
 7. The method of claim 6, wherein the two dimensional array is a binary array.
 8. The method of claim 6, further comprising: calculating a local weight for a plurality of entries in the two dimensional array; calculating a global weight for the plurality of entries in the two dimensional array; and reducing a dimension of the two dimensional array, based at least in part on the calculated local weights and global weights, wherein generating, using the computer processor, the plurality of clusters of replacement parts is based on the reduced two dimensional array.
 9. The method of claim 1, further comprising: determining a price for a first bundle of the plurality of bundles of replacement parts, based at least in part on pre-determined prices for each part in the first bundle, wherein the price for the first bundle is lower than combined pre-determined prices for each part in the first bundle.
 10. The method of claim 9, wherein the price for the first bundle is determined automatically, using the computer processor, and wherein the price for the first bundle is based on one or more characteristics of the parts in the first bundle.
 11. A system, comprising: a processor; and a memory storing a program, which, when executed on the processor, performs an operation, the operation comprising: determining a plurality of maintenance events for a plurality of replacement parts, based on historical data related to the plurality of replacement parts, wherein each maintenance event of the plurality of maintenance events corresponds with one or more of the replacement parts, the determining comprising: identifying one or more replacement parts for a maintenance event, based on one or more replacement part events occurring within a time period related to the maintenance event; generating a plurality of clusters of replacement parts based on the plurality of maintenance events; and predicting a plurality of bundles of replacement parts, based on the clusters, wherein each bundle comprises a plurality of replacement parts.
 12. The system of claim 11, wherein the one or more replacement part events comprise at least one of sales of replacement parts or electronic searches for replacement parts, and wherein the time period relating to the maintenance event comprises a time period relating to a starting point in time for the maintenance event and an ending point in time for the maintenance event.
 13. The system of claim 11, wherein determining the plurality of maintenance events for the plurality of replacement parts further comprises: generating time series data related to the historical data; and calculating a kernel density estimate related to the time series data, wherein the time period related to the maintenance event is based on the kernel density estimate.
 14. The system of claim 11, the operation further comprising: identifying a plurality of interchangeable parts among the plurality of replacement parts, wherein the plurality of maintenance events is based on the plurality of interchangeable parts.
 15. The system of claim 11, the operation further comprising: determining a price for a first bundle of the plurality of bundles of replacement parts, based at least in part on pre-determined prices for each part in the first bundle, wherein the price for the first bundle is lower than combined pre-determined prices for each part in the first bundle.
 16. The system of claim 15, wherein the price for the first bundle is determined automatically and wherein the price for the first bundle is based on one or more characteristics of the parts in the first bundle.
 17. A computer program product for intelligently predicting bundles of replacement parts, the computer program product comprising: a non-transitory computer-readable storage medium having computer-readable program code embodied therewith, the computer-readable program code executable by one or more computer processors to perform an operation, the operation comprising: determining a plurality of maintenance events for a plurality of replacement parts, based on historical data related to the plurality of replacement parts, wherein each maintenance event of the plurality of maintenance events corresponds with one or more of the replacement parts, the determining comprising: identifying one or more replacement parts for a maintenance event, based on one or more replacement part events occurring within a time period related to the maintenance event; generating a plurality of clusters of replacement parts based on the plurality of maintenance events; and predicting a plurality of bundles of replacement parts, based on the clusters, wherein each bundle comprises a plurality of replacement parts.
 18. The computer program product of claim 17, the operation further comprising: generating a two dimensional array relating to the plurality of maintenance events and the plurality of replacement parts, wherein identifying the plurality of clusters of replacement parts is based on the two dimensional array.
 19. The computer program product of claim 17, the operation further comprising: determining a price for a first bundle of the plurality of bundles of replacement parts, based at least in part on pre-determined prices for each part in the first bundle, wherein the price for the first bundle is lower than combined pre-determined prices for each part in the first bundle.
 20. The computer program product of claim 19, wherein the price for the first bundle is determined automatically and wherein the price for the first bundle is based on one or more characteristics of the parts in the first bundle. 